Looping over dataframe and selecting rows based on substring in Pandas

Question

I have a dataframe with 5 columns, one of which is 'TABLE_NAME'. That column has values such as:

A_value1
B_value1
B_value2
A_value150

I want to print those that start with 'A_' only.

I tried this but its returning the following:

ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Here is the code:

value = 'A_'
for index, row in df.iterrows():
    if df.loc[df['TABLE_NAME'].str.contains(value)]:
        print('y')
    else:
        print('n')

Explicit loops should be a last resort, read the Pandas docs. — AMC
– AMC, Commented Jan 25, 2020 at 3:40
Does this answer your question? Truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all() — AMC
– AMC, Commented Jan 25, 2020 at 3:40

Allen Qin · Accepted Answer · 2020-01-25 02:00:54Z

1

You can use str.startswith:

df.loc[df.TABLE_NAME.str.startswith('A_')]

If you want to use your for loop, you can do:

value = 'A_'
for index, row in df.iterrows():
    if row['TABLE_NAME'].startswith(value):
        print('y')
    else:
        print('n')

answered Jan 25, 2020 at 2:00

Allen Qin

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